As is known in the art, sensor integrated path planning has been a central area of research for autonomous robotic systems for many decades. In recent years, goal-oriented path planning with obstacle avoidance has gained widespread exposure (see e.g., the 2004 and 2007 DARPA Unmanned Ground Vehicle (UGV) challenges). More recently, due at least in part to improvements in power sources (e.g., lithium polymer batteries) and less expensive motors (e.g., brushless motors), task planning for small multirotor platforms has become an active area of autonomous systems research.
One common method for aerial Intelligence, Search, and Reconnaissance (ISR) missions over a designated area of interest is to use pre-programmed flight paths that cover a specified area. “Lawnmower” path or polygonal flight paths are common examples. Unmanned aerial vehicle (UAV) flight control and sensor data acquisition are typically decoupled in intelligence-related missions. In the archetypal case, a UAV flies pre-programmed Global Positioning Satellite (GPS) waypoints while an onboard sensor streams data to a ground control station observed by a human or post-processed for analysis.